A Mixture Model for Bivariate Interval-Censored Failure Times with Dependent Susceptibility

Shu Jiang, Richard J. Cook

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Interval-censored failure times arise when the status with respect to an event of interest is only determined at intermittent examination times. In settings where there exists a sub-population of individuals who are not susceptible to the event of interest, latent variable models accommodating a mixture of susceptible and nonsusceptible individuals are useful. We consider such models for the analysis of bivariate interval-censored failure time data with a model for bivariate binary susceptibility indicators and a copula model for correlated failure times given joint susceptibility. We develop likelihood, composite likelihood, and estimating function methods for model fitting and inference, and assess asymptotic-relative efficiency and finite sample performance. Extensions dealing with higher-dimensional responses and current status data are also described.

Original languageEnglish
Pages (from-to)37-62
Number of pages26
JournalStatistics in Biosciences
Issue number1
StatePublished - Apr 1 2020


  • Copula
  • Estimating functions
  • Interval-censored
  • Multivariate
  • Nonsusceptible
  • Two-stage estimation


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